Description Usage Arguments Details Value Author(s) References See Also Examples

Random generation and density function for the finite mixture of Gamma distribution.

1 2 3 |

`n ` |
The number of samples required. |

`x ` |
The vector of quantiles. |

`weight` |
The vector of probability weights, with length equal to number of components ( |

`alpha ` |
The vector of non-negative parameters of the Gamma distribution. |

`beta ` |
The vector of non-negative parameters of the Gamma distribution. |

Sampling from finite mixture of Gamma distribution, with density:

*Pr(x|\underline{w}, \underline{α}, \underline{β}) = ∑_{i=1}^{k} w_{i} Gamma(x|α_{i}, β_{i}),*

where

*Gamma(x|α_{i}, β_{i})=\frac{(β_{i})^{α_{i}}}{Γ(α_{i})} x^{α_{i}-1} e^{-β_{i}x}.*

Generated data as an vector with size *n*.

Reza Mohammadi [email protected]

Mohammadi, A., Salehi-Rad, M. R., and Wit, E. C. (2013) Using mixture of Gamma distributions for Bayesian analysis in an M/G/1 queue with optional second service. *Computational Statistics*, 28(2):683-700

Mohammadi, A., and Salehi-Rad, M. R. (2012) Bayesian inference and prediction in an M/G/1 with optional second service. *Communications in Statistics-Simulation and Computation*, 41(3):419-435

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Not run:
n = 10000
weight = c( 0.6 , 0.3 , 0.1 )
alpha = c( 100 , 200 , 300 )
beta = c( 100/3, 200/4, 300/5 )
data = rmixgamma( n = n, weight = weight, alpha = alpha, beta = beta )
hist( data, prob = TRUE, nclass = 30, col = "gray" )
x = seq( -20, 20, 0.05 )
densmixgamma = dmixnorm( x, weight, alpha, beta )
lines( x, densmixgamma, lwd = 2 )
## End(Not run)
``` |

```
Loading required package: BDgraph
Loading required package: Matrix
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Attaching package: 'BDgraph'
The following object is masked from 'package:igraph':
compare
```

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